In the field of artificially intelligent computer systems capable of answering questions posed in natural language, cognitive question answering (QA) systems (such as the IBM Watson™ artificially intelligent computer system or and other natural language question answering systems) process questions posed in natural language to determine answers and associated confidence scores based on knowledge acquired by the QA system. In operation, users submit one or more questions through a front-end application user interface (UI) or application programming interface (API) to the QA system where the questions are processed to generate answers or responses that are returned to the user(s). The QA system generates multiple hypothesis in the form of answers and associated confidence measures and supporting evidence by applying a natural language process to an ingested knowledge base (also known as the corpus) which can come from a variety of sources, including publicly available information and/or proprietary information stored on one or more servers, Internet forums, message boards, or other online or networked information sources. However, the quality of the answer to an individual user depends in part on what specific information and/or answer format that user is seeking. With traditional QA systems, there is only one mode or presentation style for presenting an answer or system output, even if presentation styles vary across different systems. For example, existing QA systems (e.g., Google search results) retrieve and present information results for browsing, but offer a single interface mode which is the same for all users. As a result, a conventional QA system will typically provide a single-pass user interface whereby a user asks a question, and the QA system provides an answer using the default presentation style. While this is sufficient for some users, there will be other users who may desire additional (or less) information than is provided with the answer/response using the default presentation style. As a result, the existing solutions for processing questions and answers provide no opportunity for the user to control or influence the interaction with such QA systems so that individualized solutions for efficiently presenting answer responses are extremely difficult at a practical level.